Distribuzioni per CTR

Per ogni singola caratteristica, è utile osservare sia la distribuzione della feature sia come varia rispetto alla variabile di interesse. In questo esercizio esplorerai la feature search_engine_type, che è un intero che identifica il motore di ricerca, come Google o Bing, tramite il quale l’utente ha espresso l’intento che ha portato all’annuncio. Per motivi di privacy, queste categorie sono anonime. Per prima cosa costruirai e visualizzerai la distribuzione di search_engine_type. Poi osserverai come il CTR varia in base al valore di search_engine_type, in modo simile a come hai analizzato il CTR per tipo di dispositivo e posizione del banner nel capitolo precedente.

Dati di esempio in forma di DataFrame sono caricati come df. pandas come pd è già disponibile nel tuo workspace.

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Prevedere il CTR con il Machine Learning in Python

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Prova a risolvere questo esercizio completando il codice di esempio.

# Construct bar chart for clicks by search engine type
se_df = df.____(['search_engine_type', 'click']).size().unstack()
se_df.plot(kind = 'bar', title = 'Value frequency for search engine type')
plt.show()

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Questo esercizio fa parte del corso

Prevedere il CTR con il Machine Learning in Python

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Chances are you’re on this page because you clicked a link. In this chapter, you’ll learn why click-through-rates (CTR) are integral to targeted advertising, how to perform basic DataFrame manipulation, and how you can use machine learning models to predict CTR.

Exercise 1: Introduction to click-through rates Exercise 2: Beginning steps Exercise 3: Feature exploration Exercise 4: First evaluation of data Exercise 5: Overview of machine learning models Exercise 6: Logistic regression for breast cancer Exercise 7: Logistic regression for images Exercise 8: A second toy model Exercise 9: CTR prediction using decision trees Exercise 10: Model implementation Exercise 11: A first CTR model Exercise 12: Beyond only accuracy

This chapter provides the foundations for exploratory data analysis (EDA). Using sample data you’ll use the pandas library to look at columns and data types, explore missing data, and use hashing to perform feature engineering on categorical features. All of which are important when exploring features for more accurate CTR prediction.

Exercise 1: Analisi esplorativa dei dati Exercise 2: Un primo sguardo Exercise 3: Verificare i valori mancanti Exercise 4: Distribuzioni per CTR

Esercizio in corso

Exercise 5: Feature engineering Exercise 6: Analizzare le colonne datetime Exercise 7: Conversione delle variabili categoriche Exercise 8: Creare nuove feature Exercise 9: Standardizzare le feature Exercise 10: Normalizzazione logaritmica Exercise 11: Capire la standardizzazione Exercise 12: Standard scaling

It’s time to dive deeper. Find out how you can use measures of model performance including precision and recall to answer real-world questions, such as evaluating ROI on ad spend. You’ll also learn ways to improve upon those evaluation metrics, such as ensemble methods and hyperparameter tuning.

Exercise 1: Applications of metric evaluation Exercise 2: Four categories of outcomes Exercise 3: Evaluating four categories Exercise 4: ROI on ad spend Exercise 5: Model evaluation Exercise 6: Precision and recall Exercise 7: Baseline Exercise 8: Classifier comparison Exercise 9: Tuning models Exercise 10: Regularization Exercise 11: Cross validation Exercise 12: Model selection Exercise 13: Ensembles and hyperparameter tuning Exercise 14: Understanding hyperparameter tuning Exercise 15: Random forests Exercise 16: Grid search

Profits can be heavily impacted by your campaign’s CTR. In this chapter, you’ll learn how deep learning can be used to reduce that risk. You’ll focus on multi-layer perceptron (MLP) and neural network models, and learn how these can be used to capture the complex relationship between variables to more accurately predict CTR. Lastly, you’ll explore how to apply the basics of hyperparameter tuning and regularization to classification models.

Exercise 1: Introduction to deep learning Exercise 2: Understanding MLPs Exercise 3: Beginning model Exercise 4: MLPs for CTR Exercise 5: Hyperparameter tuning in deep learning Exercise 6: Hyperparameter tuning in MLPs Exercise 7: Varying hyperparameters Exercise 8: MLP Grid Search Exercise 9: Model evaluation Exercise 10: F-beta score Exercise 11: Low precision and high AUC Exercise 12: Precision, ROI, and AUC Exercise 13: Model review and comparison Exercise 14: Model comparison warmup Exercise 15: Evaluating precision and ROI Exercise 16: Total scoring Exercise 17: Wrap-up video